Machine learning–based integration of surface morphometric features identifies diagnostic and symptom-related cortical signatures of schizophrenia

Jo, Han-Gue; Votinov, Mikhail; Habel, Ute; Wagels, Lisa; Hüpen, Philippa

doi:0920-9964

Machine learning–based integration of surface morphometric features identifies diagnostic and symptom-related cortical signatures of schizophrenia

Jo, Han-Gue (Corresponding author) ; Votinov, Mikhail^RWTH* ; Hüpen, Philippa^RWTH* ; Wagels, Lisa^RWTH* ; Habel, Ute^RWTH*

In
Schizophrenia research 297, Seiten/Artikel-Nr.:120-128

2026

ImpressumAmsterdam [u.a.] : Elsevier Science

ISSN1573-2509

Online
DOI: 10.1016/j.schres.2026.07.031
10.1016/j.schres.2026.07.031

Einrichtungen

Thematische Einordnung (Klassifikation)
DDC: 610

Dokumenttyp
Journal Article

Format
online, print

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-105046563887
WOS Core Collection: WOS:001845750000001

Interne Identnummern
RWTH-2026-08134
Datensatz-ID: 1040748

Beteiligte Länder
Germany, South Korea

Lizenzstatus der Zeitschrift

; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Current Contents - Social and Behavioral Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz